Every blog post about LeetCode-style interviews tells you the same thing: which problems to grind, which patterns to memorize, which study plan to follow. That advice is fine, but it is not what separates the candidates who pass from the candidates who do not.

I run multiple technical interviews a week as a senior software engineer at a large defense tech company. Most of the candidates I interview do not pass. The ones who fail are not, on average, less smart than the ones who pass. They run out of time. They burn fifteen minutes fighting their language’s syntax, ten more chasing an optimal solution they cannot finish, and then panic in the last five minutes when nothing compiles.

You have a fixed amount of time in a coding interview — usually 45 minutes. Your job is to maximize the slice of that time you spend actually solving the problem. Everything below is about that one goal.

1. Make Python your default language

I know you have a favorite language. I know your interviewer will say “use whatever you’re most comfortable with.” Use Python anyway.

There is no debate. The best language for a timed coding interview is Python, because Python costs you the fewest keystrokes per idea. A Hello, World is one line. A list comprehension replaces six lines of Java. Reading from a dict is one character. Reversing a list is [::-1].

Every keystroke you save is a keystroke you can spend thinking. In a 45-minute interview, the engineer using Python is effectively interviewing for an hour while the engineer using Java is interviewing for 35 minutes. That gap decides outcomes.

The exception: if the role is explicitly for a language Python cannot do justice to (embedded C, Go on a systems team, etc.), use that language. Otherwise, default to Python.

2. Master the Python basics until they are reflex

Did I mention you should use Python? Use Python. But Python only buys you time if you do not have to think about Python.

The candidates who waste the most time in my interviews are not the ones who do not know algorithms. They are the ones who know the algorithm and then spend four minutes Googling around in their head trying to remember how to slice a list, or whether dict.get returns None or throws.

You need the following to be muscle memory. Not “I can figure it out in 30 seconds” — reflex:

• Initializing tuples, lists, dicts, and sets (and the difference between {} and set()).
• Adding and removing from each: append, pop, extend, add, discard, remove, del, popitem.
• Slicing: arr[i:j], arr[::-1], arr[:-1], arr[::2]. All of it.
• Shallow vs. deep copy: when does mutating a copy mutate the original? You should never have to think about it mid-interview.
• Stacks and queues as lists (append / pop for stacks) and collections.deque for queues.
• Heaps with heapq — and the trick for max-heaps (push the negative).
• Sorting with custom keys: sorted(items, key=lambda x: (x.priority, -x.timestamp)).
• Common patterns: enumerate, zip, defaultdict, Counter.

If you cannot type these in your sleep, you are not ready. This is the highest-leverage thing you can drill, and almost nobody does it intentionally.

3. Start with the brute force. Always.

For the love of God, start with the brute force.

Do not think about the optimal solution. Do not. Get the brute force working first. The brute force is your insurance policy: it proves you understand the problem, it gives you something correct to point at, and it gives you a baseline to optimize from.

I have watched dozens of candidates spend 25 minutes silently staring at the whiteboard trying to derive an O(n log n) solution from scratch, then panic-code something broken in the last 10 minutes. They walk out thinking they failed because the problem was too hard. They failed because they refused to write the obvious nested loop.

The order is: understand the problem → brute force → test it → then, and only then, optimize.

4. A correct first-try solution beats an optimal broken one

Your interviewer is more impressed by a correct, working brute-force solution on the first run than by a half-finished “optimal” solution that does not compile.

Here is the reason, from someone who fills out the scorecard: when you submit a correct brute force, I check the box for problem solving, the box for coding ability, and the box for communication. We then have a conversation about how to optimize it, and I check the box for optimization based on that conversation. You pass.

When you submit a broken optimal attempt, I cannot check any of those boxes confidently. I do not know if you understand the problem. I do not know if you can write working code. I just know you wrote 40 lines of something that does not run.

If the interviewer wants the optimal solution, they will tell you. They will either stop you before you start coding (“can you think about a more efficient approach first?”) or ask you after the brute force is in (“nice, how would you make this faster?”). They will tell you. Until they do, your job is to ship a correct answer.

Correct output beats optimal Big-O. Internalize that.

5. Master LeetCode 75 — but you already knew that

Every other blog post will tell you to grind LeetCode, so I am not going to spend much time here. The minimum bar: you should be able to solve any random medium-difficulty LeetCode problem in 30 minutes.

LeetCode 75 is a solid curated set to drill against. If you cannot consistently get through mediums on that list, you are not ready for a real interview loop yet. Keep practicing.

6. Master the core problem patterns

Most interview problems are one of a small handful of patterns wearing different costumes. Recognize the pattern in the first two minutes and you have already won most of the battle. Fail to recognize it and you will reinvent the algorithm from first principles, which is exactly the time sink we are trying to avoid.

The patterns you must own:

• Two pointers — sorted arrays, palindromes, partitioning.
• Sliding window — substrings, subarrays with a constraint.
• Binary search — sorted data, and the harder cases where you binary-search the answer space.
• BFS vs. DFS — and when to use which. BFS for shortest path in an unweighted graph. DFS for path enumeration, backtracking, topological sort, cycle detection. If you flip a coin between them, you will get the wrong one half the time.
• Backtracking — permutations, combinations, constraint satisfaction (N-queens, Sudoku).
• Dynamic programming — both memoization (top-down) and tabulation (bottom-up). Know how to spot overlapping subproblems.
• Heaps — top-K, scheduling, merging sorted streams.
• Graphs — adjacency list representation, Dijkstra, Union-Find.

You do not need to memorize implementations. You need to recognize, within the first read of the problem, which pattern applies. That recognition is what fast interview performance looks like from the outside.

The thread that ties it all together

Every tip on this list is the same tip: reduce the friction between your brain and a working solution, so the fixed clock buys you more thinking time.

• Python instead of Java → fewer keystrokes per idea.
• Python basics as reflex → no syntax tax.
• Brute force first → no time wasted on the wrong target.
• Correct over optimal → time spent on what actually scores.
• LeetCode 75 → no time wasted relearning the medium tier mid-interview.
• Pattern recognition → no time wasted reinventing algorithms.

The candidates who pass my interviews are not smarter than the ones who do not. They are faster — not because they type faster, but because they have removed every source of friction between reading the problem and writing the answer. That is a thing you can practice. Most candidates do not.

If you do, your odds go up a lot.